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A hybrid approach based on BOCR and fuzzy MULTIMOORA for logistics service provider selection

Abstract: Partner selection is critical to developing successful collaboration for gaining competitive advantage in the logistics industry. In this paper, we present a hybrid approach based on BOCR and MULTIMOORA for the logistics service provider selection. The proposed approach comprises three steps. In the first step, we identify the partner selection criteria using four categories namely benefits, costs, opportunities and risks (BOCR). The second step involves generating linguistic ratings for potential partners on the identified criteria by a committee of decision-making experts. In the third and the last step, final partner selection is done using fuzzy MULTIMOORA. Linguistic information (fuzzy numbers) is used to address the lack of quantitative data. A numerical application is provided. Monte Carlo simulationbased sensitivity analysis is conducted to determine the robustness of MULTIMOORA to variation in criterion and decision maker weights. The strength of our work is the ability to perform logistics partner selection under limited or lack of quantitative data. Besides, BOCR technique allows evaluation of logistics partners from multiple perspectives namely benefits, costs, opportunities and risks. The use of MULTIMOORA technique permits the generation of robust alternative rankings due to incorporation of three inbuilt evaluation functions.


Keywords: logistics; partner selection; multicriteria decision-making; BOCR; fuzzy logic; MULTIMOORA; sensitivity analysis.


Awasthi, A. and Balezentis, T. (2017) A hybrid approach based on BOCR and fuzzy MULTIMOORA for logistics service provider selection, In International Journal of Logistics Systems and Management (IJLSM), Vol. 27, No. 3, pp.261–282. DOI:http://dx.doi.org/10.1504/IJLSM.2017.10005115. ISSN online: 1742-7975 ISSN print: 1742-7967. [Scopus (Elsevier); Academic OneFile (Gale); cnpLINKer (CNPIEC); Expanded Academic ASAP (Gale); Business Collection (Gale)].



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Available online 15 May 2017


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